LangChain, Vector Databases, and Retrieval-Augmented Generation

As enterprises adopt Large Language Models (LLMs) for automation and intelligence, Retrieval-Augmented Generation (RAG) and vector databases are redefining how businesses manage knowledge and leverage private data securely. LangChain, an open-source framework, simplifies the process of building intelligent, data-aware applications by connecting LLMs with structured and unstructured enterprise data sources. This 7-day corporate training program provides hands-on experience in designing and deploying advanced AI applications using LangChain, RAG architectures, and vector databases like FAISS, Pinecone, and Weaviate. Participants will learn to build enterprise-grade chatbots, document search engines, and AI assistants that can interact with proprietary datasets efficiently and securely.

Objectives of the Training

Understand the concepts of Retrieval-Augmented Generation (RAG) and vector search.
Learn how LangChain integrates with LLMs to create context-aware applications.
Gain hands-on experience with vector databases such as FAISS, Pinecone, and Weaviate.
Build and optimize AI-powered search and conversational systems for enterprise use cases.
Implement scalable and secure deployment of RAG-based applications on cloud environments.

Prerequisites

Strong understanding of Python programming and APIs.
Familiarity with LLMs and NLP concepts.
Basic knowledge of machine learning and database management systems.
Experience with cloud services (AWS, Azure, or GCP) is beneficial.

What You Will Learn

Fundamentals of RAG architecture and LangChain framework.
Working with embeddings, similarity search, and vector indexing.
Implementing document retrieval, chunking, and query optimization.
Integrating LLMs with enterprise data systems for knowledge management.
Building and deploying production-ready RAG pipelines for intelligent automation.

Target Audience

This program is designed for AI Engineers, Data Scientists, NLP Specialists, and Solution Architects who aim to develop LLM-powered enterprise applications. It is also valuable for Innovation Managers and Product Leaders seeking to transform business processes using conversational and retrieval-based AI solutions.

Detailed 7-Day Curriculum

Day 1 – Introduction to RAG, LangChain, and Vector Search (6 Hours)

Session 1: Overview of RAG and Its Role in Enterprise AI.
Session 2: Introduction to LangChain and Its Ecosystem.
Session 3: Understanding Vector Databases – Concepts, Use Cases, and Architecture.
Hands-on: Setting up LangChain and Building a Simple Retrieval Pipeline.

Day 2 – Working with Embeddings and Vector Databases (6 Hours)

Session 1: Understanding Embeddings and Dimensionality Reduction.
Session 2: Vector Indexing and Similarity Search Techniques.
Session 3: Implementing FAISS, Pinecone, and Weaviate for Scalable Search.
Workshop: Creating and Querying a Vector Store for Enterprise Documents.

Day 3 – Advanced LangChain Components and Tools (6 Hours)

Session 1: Deep Dive into LangChain Modules – Chains, Agents, and Memory.
Session 2: Integrating External Data Sources with APIs and Connectors.
Session 3: Building Conversational AI Pipelines using LangChain and OpenAI APIs.
Hands-on: Designing a Contextual Enterprise Chatbot with Memory.

Day 4 – Building Retrieval-Augmented Generation Pipelines (6 Hours)

Session 1: RAG Architecture – Retrieval, Context Injection, and Generation.
Session 2: Chunking, Document Splitting, and Context Optimization Strategies.
Session 3: Integrating RAG with LLMs for Contextual Responses.
Case Study: AI-Powered Knowledge Retrieval System for Enterprise Support.

Day 5 – Optimization, Evaluation, and Monitoring (6 Hours)

Session 1: Evaluating RAG Models – Relevance, Precision, and Latency Metrics.
Session 2: Improving Performance using Caching, Re-ranking, and Embedding Optimization.
Session 3: Logging, Monitoring, and Feedback Loops for Continuous Improvement.
Workshop: Tuning RAG Pipeline Performance with LangSmith and Tracing Tools.

Day 6 – Cloud Deployment and Security (6 Hours)

Session 1: Deploying RAG Applications on AWS, Azure, or GCP.
Session 2: Integrating with APIs, Containers, and Serverless Functions.
Session 3: Data Privacy, Governance, and Security for Enterprise AI Systems.
Hands-on: Deploying a Secure RAG Chatbot with API Gateway and Authentication.

Day 7 – Capstone Project & Future of RAG Systems (6 Hours)

Session 1: Capstone Project – Designing a RAG System for an Enterprise Scenario.
Session 2: Presentation and Peer Evaluation of RAG Applications.
Session 3: Future Trends – Hybrid RAG Architectures, Multi-Modal Retrieval, and AI Agents.
Panel Discussion: The Role of RAG in Next-Gen Enterprise Intelligence.

Capstone Project

Participants will design and implement a fully functional RAG application tailored to an enterprise use case. Potential projects include intelligent document search engines, domain-specific chatbots, or automated data query assistants. Each project will include vector database integration, context retrieval logic, and an optimized LLM response workflow.

Future Trends in LangChain, Vector Databases, and RAG

The future of enterprise AI lies in context-driven intelligence, where RAG systems enable LLMs to leverage private and dynamic knowledge. Emerging innovations include multi-modal retrieval (text, image, and audio), hybrid RAG with reinforcement feedback, and agentic AI capable of autonomous data reasoning. Enterprises adopting RAG architectures will lead in deploying scalable, explainable, and high-accuracy AI assistants across industries.